GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Bohai University · Collaborative Innovation Center of Advanced Microstructures · +10 more institutions
Abstract
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in Fan et al. [Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models and demonstrate…
Citation impact
- FWCI
- 23.66
- Percentile
- 100%
- References
- 161
Authors
20- ZFZheyong FanCorresponding
Bohai University
- YWYanzhou Wang
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, University of Science and Technology Beijing, Aalto University
- PYPenghua Ying
Harbin Institute of Technology
- KSKeke Song
University of Science and Technology Beijing
- JWJunjie Wang
Collaborative Innovation Center of Advanced Microstructures, Nanjing University
Topics & keywords
- Python (programming language)
- Computer science
- Workflow
- Computational science
- Graphics
- Artificial intelligence
- Machine learning
- Algorithm